Multi-task learning is a type of transfer learning that trains multiple tasks simultaneously and leverages the shared information between related tasks to improve the generalization performance. Since different tasks may have different numbers of observations, missing target values can be handled in multi-task learning. Missing features in the input matrix used to predict the target value, however, need to be addressed.
Missing features (incomplete data) occur for different reasons in a wide variety of applications. For example, in clinical studies, some patients fail to answer some questions, or measurements of some biospecimens are partially lost at various stages. In bioinformatics, experimentation errors, image corruption, and damage to a slide cause missing gene expression samples. In image processing, objects may be partially obstructed from view result in incomplete observations. Moreover, malfunctioning or unreliable sensors in sensor networks may result in missing data.
Using some machine learning techniques, each observation with a missing feature may be deleted entirely. For small datasets, deleting observations may result in a dataset that is statistically non-representative or biased or result in considerable error in the trained model. As another option, instead of deleting the observation vector, a zero or other constant value possibly specified by a user may be used to provide the missing feature value. In some machine learning techniques, a value may be imputed for each missing feature before applying machine learning. For example, a mean value computed for the feature may be used to provide the missing feature value. As another option, a feature value from a most recent observation may be used to provide the missing feature value. As still another option, a matrix completion method may be used to provide the missing feature value. Imputation methods may neglect an uncertainty of missing values by replacing them with fixed instances, inducing bias and underrating data variability. Imputation methods further may be based on assumptions that are not valid in many cases.